It is known in the art to cause a computer to emulate certain functions that are traditionally associated with human behavior. For example, efforts at artificial intelligence (“AI”) generally attempt to provide knowledge in response to inquiries. However, known AI systems merely respond with information that has been programmed into them. That is, a machine programmed with an AI program merely responds in the manner in-which its human programmers provided for when the program was written.
Experiments in the field of artificial life (“AL”) attempt to cause a machine to function or respond to external stimuli in a manner generally associated with a living organism. While such experiments are providing a foundation for future useful devices, the machine/human interface is not much enhanced by the present state of the art in this field.
It is recognized in the field that it would be valuable to have a computer which does not respond in some preprogrammed automatic manner. Genetic algorithms have been devised which attempt to get around this problem by emulating or recapitulating evolution, in the hope that eventually intelligence will emerge. Neural networks have attempted to do something similar by emulating the function of neurons in higher life forms. While it is possible that these methods might eventually help to solve some aspect of the problem, there has not yet been any useful benefit derived from such experiments.
It would be beneficial to have a machine/human interface which approaches the flexibility of a human/human interface. However, all known efforts in the field have been limited to either providing a particular preprogrammed response to an inquiry, or else have not provided a useful interface between a user and the machine.